Unsup Simcse Ja Large

🚀 unsup-simcse-ja-large

This is a model for feature extraction and sentence similarity tasks, leveraging sentence-transformers and based on the Transformer architecture.

🚀 Quick Start

Prerequisites

Before using the model, you need to install the necessary libraries.

📦 Installation

If you have sentence-transformers installed, using this model becomes straightforward:

pip install -U fugashi[unidic-lite] sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
sentences = ["こんにちは、世界！", "文埋め込み最高！文埋め込み最高と叫びなさい", "極度乾燥しなさい"]

model = SentenceTransformer("cl-nagoya/unsup-simcse-ja-large")
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage

Without sentence-transformers, you can use the model as follows: First, pass your input through the transformer model, then apply the appropriate pooling operation on top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch


def cls_pooling(model_output, attention_mask):
    return model_output[0][:,0]


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("cl-nagoya/unsup-simcse-ja-large")
model = AutoModel.from_pretrained("cl-nagoya/unsup-simcse-ja-large")

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

🔧 Technical Details

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Model Summary

See the GitHub repository for a detailed experimental setup.

📄 License

This model is released under the cc-by-sa-4.0 license.

Citing & Authors

@misc{
  hayato-tsukagoshi-2023-simple-simcse-ja,
  author = {Hayato Tsukagoshi},
  title = {Japanese Simple-SimCSE},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/hppRC/simple-simcse-ja}}
}